What Data for This Category Looks Like
Typical entities for this category are smelting, electrolysis, laboratory, and scheduling departments of copper, aluminum, lead-zinc, and other smelting enterprises. Daily generated documents include batching lists, smelting records, electrolytic cell condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data sources cover concentrate and auxiliary material analysis reports, DCS system data, cell voltage and current monitoring data, laboratory test results, and energy and environmental protection monitoring records. Data update frequency changes with business nodes, updating with each batch of raw materials, each furnace, or each cell output. Key monitoring indicators update in real time. The data includes structured numerical fields and unstructured process descriptions. Some business verification tasks require associating multi-source data to complete.
Constraints on Knowledge Base Retrieval and Recall From These Characteristics
Data for this category is multi-source and heterogeneous, with uneven update frequencies and high matching accuracy requirements, creating multiple constraints for retrieval and recall. Classify and retrieve multi-source data according to business scenarios to avoid irrelevant data interference across sources. Real-time updated monitoring data requires retrieval indexes to support incremental refresh, and cannot rely on fixed-cycle full reconstruction. Business issues often involve professional scenarios such as complex component balance and furnace condition adjustment, requiring precise matching of professional terms and historical records to avoid low-relevance results. The data volume of a single batch of business is large, so the number of retrieved results must be controlled within a reasonable range to avoid exceeding the context processing limit.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.88 | This category’s laboratory data and electrolytic cell condition records have high matching accuracy requirements, requiring filtering of low-relevance historical logs and non-target business records |
limit | Top 10–15 entries | The volume of associated data for a single batch of smelting or electrolysis business is large, controlling the number of returned entries avoids exceeding the context processing limit |
usingReRank | Enabled | In multi-source mixed data scenarios, reranking improves the recall accuracy of key indicators such as cell voltage and metal grade, and optimizes result sorting logic |
datasetSearchUsingExtensionQuery | Enabled | Business issues for this category often involve professional scenarios such as component balance and furnace condition adjustment, extending queries can cover synonyms or related terms to improve recall coverage |
collectionIds | Configured by data source groups, such as separately associating smelting record, electrolytic cell condition, and laboratory report collections | Data from different data sources has different business relevance, grouped retrieval can accurately match the needs of current business scenarios |
Three Common Misconfigurations
- A 400 status code is returned when calling the
pushDatainterface for batch data upload, and the interface prompts "Data batch exceeds upper limit". The cause is failure to split upload batches according to the maximum 200 groups per batch rule, resulting in exceeding the interface limit. - After configuring the internet search tool, retrieving business issues returns no results, and the interface displays "No matching results". The cause is that core business data for this scenario is all stored in the local knowledge base, and local knowledge base priority retrieval is not configured, or the only internet search mode is mistakenly enabled.
- When the local knowledge base is configured for public access, sensitive environmental protection ledgers and laboratory data are retrieved via the public internet. External users can access dataset content through public links. The cause is failure to configure dataset access permissions as private, leading to data leakage risks.
How to Confirm Configuration Is Correct
- After uploading a single batch of 200 groups of data, check that the
pushDatainterface returns a 200 status code to confirm that the batch upload configuration takes effect. - Initiate a query containing "abnormal electrolytic cell voltage", check that returned results only include electrolytic cell condition records and related laboratory data, with no irrelevant business content.
- After enabling
usingReRank, compare result sorting before and after enabling to confirm that key indicators such as cell voltage and metal grade appear first. - Check the FastGPT v4.17.0 dataset index update log to confirm that real-time or batch update frequency matches business node rhythm.
The values provided here are common starting points and should be validated against local deployment samples.